PREDICTION OF THE DENSITY OF STATES (DOS) OF MATERIALS USING CRYSTAL GRAPH CONVOLUTIONAL NEURAL NETWORKS (CGCNN)

PREDICTION OF THE DENSITY OF STATES (DOS) OF MATERIALS USING CRYSTAL GRAPH CONVOLUTIONAL NEURAL NETWORKS (CGCNN)

This study explores the capability of the Crystal Graph Convolutional Networks (CGCNN) model, which was previously used only for predicting single material properties such as Fermi energy, to predict sequential data properties of materials, such as the Density of States (DOS). The method employed...

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Bibliographic Details
Main Author:	Fauzi, Akmal
Format:	Theses
Language:	Indonesia
Online Access:	https://digilib.itb.ac.id/gdl/view/86938
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Institut Teknologi Bandung
Language:	Indonesia

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